RIS-aided Latent Space Alignment for Semantic Channel Equalization

TL;DR

This paper introduces a RIS-assisted joint physical and semantic channel equalization framework for semantic communication systems, improving mutual understanding in multi-user MIMO channels by aligning latent representations.

Contribution

It proposes a novel joint equalization approach combining physical and semantic layers using RIS, with both linear and DNN-based solutions, addressing semantic mismatches in multi-user settings.

Findings

Joint equalization outperforms traditional disjoint methods.

Both linear and DNN-based equalizers effectively reduce semantic mismatch.

Framework adapts well across various wireless channel conditions.

Abstract

Semantic communication systems introduce a new paradigm in wireless communications, focusing on transmitting the intended meaning rather than ensuring strict bit-level accuracy. These systems often rely on Deep Neural Networks (DNNs) to learn and encode meaning directly from data, enabling more efficient communication. However, in multi-user settings where interacting agents are trained independently-without shared context or joint optimization-divergent latent representations across AI-native devices can lead to semantic mismatches, impeding mutual understanding even in the absence of traditional transmission errors. In this work, we address semantic mismatch in Multiple-Input Multiple-Output (MIMO) channels by proposing a joint physical and semantic channel equalization framework that leverages the presence of Reconfigurable Intelligent Surfaces (RIS). The semantic equalization is implemented as a sequence of transformations: (i) a pre-equalization stage at the transmitter; (ii) propagation through the RIS-aided channel; and (iii) a post-equalization stage at the receiver. We formulate the problem as a constrained Minimum Mean Squared Error (MMSE) optimization and propose two solutions: (i) a linear semantic equalization chain, and (ii) a non-linear DNN-based semantic equalizer. Both methods are designed to operate under semantic compression in the latent space and adhere to transmit power constraints. Through extensive evaluations, we show that the proposed joint equalization strategies consistently outperform conventional, disjoint approaches to physical and semantic channel equalization across a broad range of scenarios and wireless channel conditions.